from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-16 21:58:32.699649
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Mon, 16, Nov, 2020
Time: 21:58:36
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -42.0621
Nobs: 112.000 HQIC: -43.3602
Log likelihood: 1137.52 FPE: 6.10691e-20
AIC: -44.2466 Det(Omega_mle): 2.82843e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.816510 0.229929 3.551 0.000
L1.Burgenland 0.171864 0.094172 1.825 0.068
L1.Kärnten -0.312505 0.078907 -3.960 0.000
L1.Niederösterreich 0.035179 0.229207 0.153 0.878
L1.Oberösterreich 0.245603 0.185629 1.323 0.186
L1.Salzburg 0.109544 0.094355 1.161 0.246
L1.Steiermark 0.026734 0.133557 0.200 0.841
L1.Tirol 0.159102 0.087613 1.816 0.069
L1.Vorarlberg 0.012911 0.088194 0.146 0.884
L1.Wien -0.222073 0.180574 -1.230 0.219
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.836221 0.294147 2.843 0.004
L1.Burgenland -0.002323 0.120473 -0.019 0.985
L1.Kärnten 0.349861 0.100945 3.466 0.001
L1.Niederösterreich 0.056813 0.293223 0.194 0.846
L1.Oberösterreich -0.232046 0.237474 -0.977 0.328
L1.Salzburg 0.157876 0.120708 1.308 0.191
L1.Steiermark 0.186456 0.170859 1.091 0.275
L1.Tirol 0.131999 0.112083 1.178 0.239
L1.Vorarlberg 0.179550 0.112826 1.591 0.112
L1.Wien -0.625954 0.231007 -2.710 0.007
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.349439 0.096974 3.603 0.000
L1.Burgenland 0.106881 0.039717 2.691 0.007
L1.Kärnten -0.022576 0.033279 -0.678 0.498
L1.Niederösterreich 0.127819 0.096669 1.322 0.186
L1.Oberösterreich 0.260976 0.078290 3.333 0.001
L1.Salzburg -0.000727 0.039795 -0.018 0.985
L1.Steiermark -0.060131 0.056328 -1.068 0.286
L1.Tirol 0.093249 0.036951 2.524 0.012
L1.Vorarlberg 0.146747 0.037196 3.945 0.000
L1.Wien 0.008378 0.076158 0.110 0.912
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.225627 0.116902 1.930 0.054
L1.Burgenland 0.004638 0.047879 0.097 0.923
L1.Kärnten 0.038914 0.040118 0.970 0.332
L1.Niederösterreich 0.078276 0.116535 0.672 0.502
L1.Oberösterreich 0.351275 0.094379 3.722 0.000
L1.Salzburg 0.093540 0.047973 1.950 0.051
L1.Steiermark 0.195140 0.067904 2.874 0.004
L1.Tirol 0.024939 0.044545 0.560 0.576
L1.Vorarlberg 0.111529 0.044840 2.487 0.013
L1.Wien -0.122900 0.091809 -1.339 0.181
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.928948 0.249008 3.731 0.000
L1.Burgenland 0.063783 0.101986 0.625 0.532
L1.Kärnten -0.014886 0.085455 -0.174 0.862
L1.Niederösterreich -0.147304 0.248227 -0.593 0.553
L1.Oberösterreich 0.048505 0.201033 0.241 0.809
L1.Salzburg 0.041111 0.102185 0.402 0.687
L1.Steiermark 0.109458 0.144640 0.757 0.449
L1.Tirol 0.232930 0.094883 2.455 0.014
L1.Vorarlberg 0.030508 0.095513 0.319 0.749
L1.Wien -0.256268 0.195558 -1.310 0.190
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.197105 0.174568 1.129 0.259
L1.Burgenland -0.053317 0.071498 -0.746 0.456
L1.Kärnten -0.014769 0.059908 -0.247 0.805
L1.Niederösterreich 0.217130 0.174020 1.248 0.212
L1.Oberösterreich 0.389764 0.140934 2.766 0.006
L1.Salzburg -0.030195 0.071637 -0.421 0.673
L1.Steiermark -0.052053 0.101400 -0.513 0.608
L1.Tirol 0.197227 0.066518 2.965 0.003
L1.Vorarlberg 0.046088 0.066959 0.688 0.491
L1.Wien 0.114739 0.137096 0.837 0.403
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.360671 0.223057 1.617 0.106
L1.Burgenland 0.045178 0.091357 0.495 0.621
L1.Kärnten -0.084870 0.076548 -1.109 0.268
L1.Niederösterreich -0.152895 0.222357 -0.688 0.492
L1.Oberösterreich -0.112749 0.180081 -0.626 0.531
L1.Salzburg 0.002962 0.091535 0.032 0.974
L1.Steiermark 0.383681 0.129565 2.961 0.003
L1.Tirol 0.541927 0.084995 6.376 0.000
L1.Vorarlberg 0.212269 0.085558 2.481 0.013
L1.Wien -0.182702 0.175177 -1.043 0.297
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.342034 0.253327 1.350 0.177
L1.Burgenland -0.023927 0.103755 -0.231 0.818
L1.Kärnten -0.077795 0.086936 -0.895 0.371
L1.Niederösterreich 0.200239 0.252532 0.793 0.428
L1.Oberösterreich 0.028965 0.204519 0.142 0.887
L1.Salzburg 0.239062 0.103957 2.300 0.021
L1.Steiermark 0.122157 0.147148 0.830 0.406
L1.Tirol 0.061703 0.096529 0.639 0.523
L1.Vorarlberg -0.010708 0.097169 -0.110 0.912
L1.Wien 0.143960 0.198950 0.724 0.469
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.759197 0.138636 5.476 0.000
L1.Burgenland -0.012583 0.056781 -0.222 0.825
L1.Kärnten -0.011457 0.047577 -0.241 0.810
L1.Niederösterreich -0.074010 0.138201 -0.536 0.592
L1.Oberösterreich 0.255190 0.111925 2.280 0.023
L1.Salzburg 0.008738 0.056892 0.154 0.878
L1.Steiermark -0.003465 0.080528 -0.043 0.966
L1.Tirol 0.076645 0.052827 1.451 0.147
L1.Vorarlberg 0.170109 0.053177 3.199 0.001
L1.Wien -0.149650 0.108877 -1.374 0.169
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.057442 -0.091360 0.195697 0.248761 0.030410 0.085077 -0.171651 0.032522
Kärnten 0.057442 1.000000 -0.089631 0.164556 0.030849 -0.166275 0.163270 -0.008341 0.249879
Niederösterreich -0.091360 -0.089631 1.000000 0.210597 0.013652 0.142184 0.072133 0.041910 0.348712
Oberösterreich 0.195697 0.164556 0.210597 1.000000 0.227972 0.266484 0.061749 0.046414 0.013002
Salzburg 0.248761 0.030849 0.013652 0.227972 1.000000 0.139880 0.028039 0.052963 -0.102215
Steiermark 0.030410 -0.166275 0.142184 0.266484 0.139880 1.000000 0.092727 0.100919 -0.221753
Tirol 0.085077 0.163270 0.072133 0.061749 0.028039 0.092727 1.000000 0.130915 0.075557
Vorarlberg -0.171651 -0.008341 0.041910 0.046414 0.052963 0.100919 0.130915 1.000000 0.041600
Wien 0.032522 0.249879 0.348712 0.013002 -0.102215 -0.221753 0.075557 0.041600 1.000000